In this article, we present a machine learning-based method to locate lightning flashes using calculations of lightning-induced voltages on a transmission line. The proposed approach takes advantage of the preinstalled voltage measurement systems on power transmission lines to get the data. Hence, it does not require the installation of additional sensors such as extremely low frequency, very low frequency, or very high frequency. The proposed model is shown to yield reasonable accuracy in estimating two-dimensional geolocations for lightning strike points for different grid sizes up to 100 × 100 km 2 . The algorithm is shown to be robust against the distance between the voltage sensors, lightning peak current, lightning current rise time, and signal to noise ratio of the input signals.

中文翻译：

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基于机器学习的利用输电线路雷电感应电压的雷电定位算法


在本文中，我们提出了一种基于机器学习的方法，通过计算传输线上的雷电感应电压来定位闪电。所提出的方法利用输电线路上预装的电压测量系统来获取数据。因此，它不需要安装额外的传感器，例如极低频、极低频或甚高频。所提出的模型在估计高达 100 × 100 km 2 的不同网格尺寸的雷击点二维地理位置方面具有合理的精度。该算法对于电压传感器之间的距离、雷电峰值电流、雷电电流上升时间和输入信号的信噪比具有鲁棒性。